Periodic and quasi-periodic responses of the Earth's outer electron belt to the solar wind with implications to space weather modeling

Details Periodic and quasi-periodic responses of the Earth's outer electron belt to the solar wind with implications to space weather modeling Periodic and quasi-periodic responses of the Earth's outer electron belt to the solar wind with implications to space weather modeling

Dec 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010phdt.......181p&link_type=abstract

ProQuest Dissertations And Theses; Thesis (Ph.D.)--University of Colorado at Boulder, 2010.; Publication Number: AAT 3433338; IS

Computer Science

Scientific paper

It has long been noted the distinct periodicities in the electron flux response in the outer belts are associated with solar wind speed periodicities. The well-organized high-speed streams in the solar wind drive the flux, most commonly resulting in 27, 13.5 and 9-day peaks in the periodogram during the declining phase of the solar cycle. The distribution of peak power across L-shells is calculated and a connection is made with the P1 peak in the impulse response function. Next, the periodogram estimates the power spectrum relating weak, diffuse periodicities in the number of coronal mass ejection occurrences to relativistic electron flux periodicities. Because coronal mass ejections occur preferentially during ascending phases of the solar cycle, this supplements the distinct periodicities from high-speed streams evident during declining phases of the solar cycle. Finally, the successful estimation of exogenous coefficients in an autoregressive exogenous (ARX) model is shown to require excitation at all pole frequencies in the transfer function between the output and the input. Specifically, the solar wind speed should excite all pole frequencies in the electron flux response for complete identification. Additionally, because only the first two autoregressive coefficients are dominant, system identification of eight coefficients in an ARX time series model via the Kalman filter suggests a lower order ARX model, such as the stochastic linear oscillator may be more appropriate.

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